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Variational Inference

1946 directly classified papers

Papers per year

Papers

Disentanglement via Latent Quantization NIPS 2023
Bounding Evidence and Estimating Log-Likelihood in VAE AISTATS 2023
Hierarchical VAEs provide a normative account of motion processing in the primate brain NIPS 2023
BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning AAAI 2023
On the Convergence of Black-Box Variational Inference NIPS 2023
Differentiable Random Partition Models NIPS 2023
Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models AAAI 2023
Learning Dynamic Latent Spaces for Lifelong Generative Modelling AAAI 2023
Training Energy-Based Normalizing Flow with Score-Matching Objectives NIPS 2023
Advancing Bayesian Optimization via Learning Correlated Latent Space NIPS 2023
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows NIPS 2023
Subspace Identification for Multi-Source Domain Adaptation NIPS 2023
A Flexible Diffusion Model ICML 2023
Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning NIPS 2023
Flat Seeking Bayesian Neural Networks NIPS 2023
Disentangled Representation for Causal Mediation Analysis AAAI 2023
Riemannian Laplace approximations for Bayesian neural networks NIPS 2023
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods NIPS 2023
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration NIPS 2023
Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions NIPS 2023
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States NIPS 2023
Gradient-Free Kernel Stein Discrepancy NIPS 2023
Variational Gaussian processes for linear inverse problems NIPS 2023
Masked Autoencoders Enable Efficient Knowledge Distillers CVPR 2023
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction CVPR 2023